The invention relates to the general field of telecommunications.
The invention relates more particularly to a method of predicting the performance of a digital communications system.
The invention applies in preferred but non-limiting manner to the context of a wireless telecommunications network, e.g. a long term evolution (LTE) network as defined by the third group partnership project (3GPP), in which it is envisaged adapting the resources used by a transmitter in order to transmit data to a receiver as a function of the quality of the radio link between the transmitter and the receiver (in other words as a function of the transmission channel between the transmitter and the receiver and as a function of performance, in particular in terms of error probability, that can be achieved over that channel by the system). By way of example, the transmitter may be a mobile terminal, and the receiver may be a base station in the wireless network controlling the cell in which the mobile terminal is to be found.
The resources used by the transmitter depend on the level of protection that it is desired to give to the data transmitted by the transmitter to the receiver. This level of protection varies as a function of the modulation and coding scheme (MCS) used by the transmitter: the greater the spectral efficiency of the MCS, the smaller the resulting data protection, and thus the quality of the radio link needs to be good in order to enable transmission to take place reliably over the link.
Adapting the radio link thus consists in adapting the instantaneous data rate on transmission to the quality of the channel by selecting an appropriate MCS for the transmitter on each transmission.
In a wireless telecommunications network, this adaptation relies on there being a feedback channel between the transmitter and the receiver, which feedback channel is generally of limited data rate. This adaptation comprises three main stages:                the transmitter sends pilot symbols that are known to the receiver;        on the basis of the pilot symbols, the receiver estimates the quality of the radio link with the transmitter. This radio link quality is representative of the performance to be expected from the transmitter under current conditions of the channel. As a function of this quality, the receiver selects an MCS that is appropriate for the transmission channel, and uses the feedback channel to give the transmitter an indication enabling it to identify the MCS; and        the transmitter transmits data to the receiver using the MCS as selected in this way.        
The strategy used for adapting the radio link depends on several factors, and in particular on the type of coding used on transmission, on the instantaneous characteristics of the transmission channel, on the type of equalizer and on the type of decoding used on reception. This strategy must enable adaptation to be performed quickly and efficiently so as to enable it to be implemented in real time in the telecommunications network, and in particular in the medium access control (MAC) layer in the mechanisms for taking decisions and allocating resources to the terminals.
Various strategies already exist in the state of the art that enable the radio link to be adapted quickly when the link is a multiple antenna channel with block fading that is selective in time and/or in frequency. Those strategies rely on abstracting the physical layer, and more precisely on semi-analytic modeling of the behavior of the receiver. This modeling is used to predict the performance of the transmission system including the transmitter and the receiver, in particular in terms of transmission error probability.
The document by E. Ohlmer and G. Fettweis entitled “Link adaptation in linearly precoded closed-loop MIMO-OFDM system with linear receivers”, Proceedings IEEE ICC'09, Dresden, Germany, June 2009, proposes modeling the physical layer of a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system implementing a linear receiver.
That modeling relies on evaluating, at the output from the linear receiver, a single signal to interference plus noise ratio (SINR) representing the quality of the radio link, and associated with an equivalent additive white Gaussian noise (AWGN) channel. This single SINR results from “compressing” a plurality of “intermediate” SINRs that are available at the output from the receiver, using a metric known as a mutual information effective SINR metric (MIESM), as described in particular in the document by K. Brueninghaus et al. entitled “Link performance models for system level simulations of broadband radio access systems”, Proceedings IEEE PIMRC'05, Berlin, Vol. 4, pp. 2306-2311, September 2005.
More precisely, for each intermediate SINR, a pre-established correspondence table (or look-up table (LUT)) is used to determine the mutual information of an equivalent AWGN channel at the intermediate SINR. Thereafter, on the assumption that the equivalent AWGN channels associated with the intermediate SINRs are parallel and independent, the mean is evaluated of the mutual information as determined in this way.
The resulting mean mutual information is transformed into a single SINR using the pre-established correspondence table. The single SINR constitutes a metric for the quality of the radio link. It can then be compared with error probability curves prepared for a variety of MCSs as a function of SINR, so as to select the MCS that presents the greatest spectral efficiency while complying with a given error probability.
Other models of the physical layer have been proposed for receivers that are more complex than linear receivers, and in particular for receivers using successive interference cancellation techniques.
Thus, the document by R. Visoz et al. (referred to below as D1) entitled “Semi-analytical performance prediction methods for iterative LMMSE-IC multiuser MIMO joint decoding”, IEEE Transactions on Communications, Vol. 58, No. 9, pp. 2576-2589, September 2010, describes modeling the physical layer and predicting performance on a channel with block fading that is selective in time or in frequency, for a multiuser MIMO communication system having a transmitter using an MCS and a non-linear iterative receiver performing an iterative technique of successive interference cancellation.
The MCS envisaged in D1 is binary interleaved coded modulation (BICM) and its space-time generalization (also known as space time bit interleaved coded modulation (STBICM)) when the transmitter has a plurality of transmit antennas and the receiver has a plurality of receive antennas.
FIG. 1 is a well-known diagram of STBICM modulation. The modulation is made up of an external binary error corrector code, specifically a binary convolutional code (CC) suitable for coding a plurality of information bits into coded bits, a space time interleaver (ST-Π) suitable for interleaving the coded bits over a plurality of transmit antennas, and one modulator per transmit antenna that is suitable for supplying the symbols of a given constellation from coded bits associated with each transmit antenna. No assumption is made about the labeling used by the modulators.
The iterative receiver comprises a multiuser detector (i.e. an equalizer), namely a linear minimum mean-square error iterative cancellation (LMMSE-IC) detector that feeds a bank of demodulators and soft-input soft-output (SISO) decoders (one demodulator and one decoder per user). Inputs and outputs are said to be “soft” when the SISO decoders receive non-binary values as inputs, such as for example probabilistic quantities, and also deliver outputs that are non-binary.
Each SISO decoder implements a BCJR algorithm (named for its inventors, Bahl, Cocke, Jelinek, and Raviv), which optimizes the bitwise maximum a posteriori (MAP) criterion. The multiuser detector, the demodulators, and the decoders exchange soft information about the coded bits and the symbols of the STBICM, at each iteration of the receiver.
More precisely, on each iteration i of the iterative receiver, each SISO decoder uses probabilistic observations and quantities representative of a priori probabilities that are available thereto about the coded bits associated with the symbols of the STBICM (as supplied by the demodulator) to evaluate probabilistic quantities representative of the a posteriori probabilities about those coded bits. These a posteriori probabilities represent the probabilities of these coded bits being transmitted. In D1, the probabilistic quantities received and supplied by each SISO decoder are log likelihood ratios (LLRs), that is probability ratios. In the description below, for simplification purposes, the logarithmic a posteriori probability ratios are abbreviated LAPPR and the log extrinsic probability ratios are abbreviated LEXTPR.
The LAPPRs estimated by the SISO decoder are used firstly to calculate variance on the STBICM symbols that is then supplied to the multiuser detector for use by that detector in iteration i+1 in order to detect the STBICM symbols, and secondly in order to calculate the LEXTPRs on the coded bits, which supply a measure of the reliability of the LAPPRs.
These LEXTPRs are used as logarithmic a priori probability ratios on the coded bits by the demodulator associated with that decoder on iteration i+1. The demodulator in turn supplies the LEXTPRs on the coded bits to the SISO decoder, which uses them as logarithmic a priori probability ratios on the coded bits during decoding performed in iteration i+1.
D1 proposes a method of predicting the performance of the transmission system on each iteration i of the iterative receiver, which relies:                on semi-analytic modeling of the behavior of the multiuser detector with the help of an SINR resulting from the compression by means of the MIESM of a plurality of SINRs calculated on the STBICM symbols at the output from the detector, for each transmit antenna and for each block of the channel with fading. These SINRs depend on the estimate of the transmission channel between the transmitter and the receiver, on an estimate of the variance of the noise (possibly also including certain sources of interference that are modeled as noise), and on the variance of the STBICM symbols at the input to the detector; and        on joint modeling of the demodulator and of the SISO decoder of each user, operating on the STBICM symbols delivered at the output from the multiuser detector, this modeling being based on analyzing the variation in the mean mutual information between the coded bits associated with each STBCIM symbol and the LEXTPRs on these coded bits as available at the output from the SISO decoder.        
The variation in the mutual information is determined by using a three-dimensional correspondence table that is pre-established using Monte-Carlo simulations, and that, for a given value of the mean mutual information between the coded bits of the STBICM symbols and the logarithmic a priori probability ratios on these coded bits at the input to the demodulator, and for a given value of the mean mutual information associated with the compressed SINR, gives a value for the mean mutual information between the coded bits of the STBICM symbols and the LEXTPRs on these coded bits as available at the output from the decoder.
It should be observed that this three-dimensional correspondence table is no longer necessary if Gray labeling is envisaged for the STBICM modulation. There is then no longer any need to track the mean mutual information between the coded bits and the logarithmic a priori probability ratios on these coded bits.
D1 also proposes using a similar correspondence table to establish the error probability at the output from the decoder on each iteration of the iterative receiver, and also the variance of the coded symbols. The variance of the coded symbols as determined on iteration i is used during iteration i+1 to estimate the compressed SINR. It should be observed that this three-dimensional correspondence table becomes two-dimensional, providing Gray labeling is envisaged for the STBICM.
The prediction method proposed in D1 makes it possible in accurate and rapid manner to estimate the performance of a communications system using, on transmission, coded modulation built up from a simple convolutional code, and using, on reception, optimum decoding of the convolutional code using the bitwise MAP criterion.
Nevertheless, the method is not suitable when, on transmission, use is made of coded modulation built up from codes that are more complex, such as composite codes, and in particular turbo-codes.
A turbo-code is an error-correcting code based on concatenating a plurality of elementary constituent codes (typically two of them), that are separated by an interleaver. The constituent codes may for example be recursive and systematic convolutional codes. Turbo-codes are known for their excellent performance, and they are in widespread use nowadays in wireless telecommunications standards, and in particular in the LTE standard.
Unlike convolutional codes, because of their special structure, turbo-codes are difficult to decode in optimum manner on the basis of a bitwise MAP criterion, even for code lengths that are quite short. That is why, in practice, recourse is had to sub-optimum iterative decoding based on using elementary SISO decoders (associated with the respective constituent codes of the turbo-code), with the decoders interchanging probabilitistic quantities on each decoding iteration.
It can readily be understood that under such circumstances, the modeling envisaged in D1 cannot take account of using such an iterative decoding scheme at the receiver.
There therefore exists a need for a method of predicting the performance of a communications system relying, on transmission, on using an STBICM built up from a turbo-code, and relying, on reception, on an iterative receiver implementing a detector performing successive interference cancellation and an iterative sub-optimum decoder.